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9b9fe26 78af9f2 9b9fe26 78af9f2 9b9fe26 78af9f2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | """Build the ViTeX-Edit-14B (Composite) baseline.
For each test clip:
1. Read source video, ViTeX-Edit-14B prediction, and the dilated text mask.
2. Color-correct the prediction inside the mask to match the source by
Reinhard-style mean+std matching in LAB space, using a 20-px band just
outside the mask as the reference (so the local lighting is captured).
3. Composite onto the source with a signed-distance feathered alpha
centered on the mask edge so the seam is smooth.
The output is a 1280x720, 24 fps, 120-frame mp4 written under
baseline_output_videos/ViTeX-14B_Corp/<id>.mp4.
"""
import argparse
import json
import os
import subprocess
from multiprocessing import Pool
import cv2
import numpy as np
def _read_video(path, max_frames=None):
cap = cv2.VideoCapture(path)
out = []
while True:
ok, f = cap.read()
if not ok:
break
out.append(cv2.cvtColor(f, cv2.COLOR_BGR2RGB))
if max_frames and len(out) >= max_frames:
break
cap.release()
return out
def _read_mask_video(path, target_h, target_w, max_frames=None):
cap = cv2.VideoCapture(path)
out = []
while True:
ok, f = cap.read()
if not ok:
break
gray = cv2.cvtColor(f, cv2.COLOR_BGR2GRAY)
if (gray.shape[0], gray.shape[1]) != (target_h, target_w):
gray = cv2.resize(gray, (target_w, target_h), interpolation=cv2.INTER_NEAREST)
out.append((gray > 127).astype(np.uint8))
if max_frames and len(out) >= max_frames:
break
cap.release()
return out
def _color_correct_lab(src_rgb, pred_rgb, mask_bin, band_width=20):
"""Reinhard-style LAB transfer using a band around the mask as reference."""
band = cv2.dilate(mask_bin, np.ones((band_width * 2 + 1, band_width * 2 + 1),
dtype=np.uint8)) - mask_bin
band_idx = band > 0
if band_idx.sum() < 100:
return pred_rgb # not enough reference, leave as-is
src_lab = cv2.cvtColor(src_rgb, cv2.COLOR_RGB2LAB).astype(np.float32)
pred_lab = cv2.cvtColor(pred_rgb, cv2.COLOR_RGB2LAB).astype(np.float32)
mean_src = src_lab[band_idx].mean(axis=0)
std_src = src_lab[band_idx].std(axis=0) + 1e-6
mean_pred = pred_lab[band_idx].mean(axis=0)
std_pred = pred_lab[band_idx].std(axis=0) + 1e-6
pred_corrected = (pred_lab - mean_pred) / std_pred * std_src + mean_src
pred_corrected = np.clip(pred_corrected, 0, 255).astype(np.uint8)
return cv2.cvtColor(pred_corrected, cv2.COLOR_LAB2RGB)
def _feathered_alpha(mask_bin, feather=4):
"""Smooth alpha centered on the mask boundary."""
sdf_in = cv2.distanceTransform(mask_bin, cv2.DIST_L2, 5)
sdf_out = cv2.distanceTransform(1 - mask_bin, cv2.DIST_L2, 5)
sdf = sdf_in - sdf_out
return np.clip((sdf + feather / 2.0) / feather, 0.0, 1.0).astype(np.float32)
def _process_frame(src_rgb, pred_rgb, mask_bin, band_width, feather):
pred_cc = _color_correct_lab(src_rgb, pred_rgb, mask_bin, band_width=band_width)
alpha = _feathered_alpha(mask_bin, feather=feather)[..., None]
out = src_rgb.astype(np.float32) * (1 - alpha) + pred_cc.astype(np.float32) * alpha
return out.astype(np.uint8)
def _encode_video(frames, out_path, fps=24):
if not frames:
raise RuntimeError("no frames to encode")
h, w = frames[0].shape[:2]
proc = subprocess.Popen([
"ffmpeg", "-y", "-loglevel", "error",
"-f", "rawvideo", "-pix_fmt", "rgb24",
"-s", f"{w}x{h}", "-r", str(fps),
"-i", "-",
"-c:v", "libx264", "-preset", "medium", "-crf", "18",
"-pix_fmt", "yuv420p", "-movflags", "+faststart",
out_path,
], stdin=subprocess.PIPE)
for f in frames:
proc.stdin.write(np.ascontiguousarray(f).tobytes())
proc.stdin.close()
if proc.wait() != 0:
raise RuntimeError(f"ffmpeg failed for {out_path}")
def _process_clip(args):
rec, data_root, pred_dir, out_dir, target_frames, band_width, feather = args
vid = rec["id"]
out_path = os.path.join(out_dir, vid + ".mp4")
if os.path.exists(out_path):
return vid, "skip"
src_path = os.path.join(data_root, rec["original_video"])
mask_path = os.path.join(data_root, rec["mask_video"])
pred_path = os.path.join(pred_dir, vid + ".mp4")
if not (os.path.exists(src_path) and os.path.exists(mask_path) and os.path.exists(pred_path)):
return vid, "missing"
src_frames = _read_video(src_path, max_frames=target_frames)
pred_frames = _read_video(pred_path, max_frames=target_frames)
if not src_frames or not pred_frames:
return vid, "empty"
h, w = src_frames[0].shape[:2]
# Pred may be smaller (e.g., other res); resample to source grid.
pred_frames = [cv2.resize(f, (w, h), interpolation=cv2.INTER_LANCZOS4)
if (f.shape[0], f.shape[1]) != (h, w) else f
for f in pred_frames]
mask_frames = _read_mask_video(mask_path, target_h=h, target_w=w, max_frames=target_frames)
n = min(len(src_frames), len(pred_frames), len(mask_frames), target_frames)
out_frames = []
for t in range(n):
out_frames.append(_process_frame(
src_frames[t], pred_frames[t], mask_frames[t], band_width, feather,
))
_encode_video(out_frames, out_path, fps=24)
return vid, f"ok ({n}f)"
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--records", required=True)
ap.add_argument("--data_root", required=True)
ap.add_argument("--pred_dir", required=True,
help="Directory of ViTeX-Edit-14B raw predictions (e.g., ViTeX-Edit-14B_orig)")
ap.add_argument("--out_dir", required=True,
help="Where the corp baseline mp4s are written")
ap.add_argument("--target_frames", type=int, default=120)
ap.add_argument("--band_width", type=int, default=20,
help="Width in px of the reference band around the mask")
ap.add_argument("--feather", type=int, default=4,
help="Feather width in px centered on the mask edge")
ap.add_argument("--workers", type=int, default=8)
args = ap.parse_args()
os.makedirs(args.out_dir, exist_ok=True)
with open(args.records) as f:
records = json.load(f)
tasks = [(r, args.data_root, args.pred_dir, args.out_dir,
args.target_frames, args.band_width, args.feather)
for r in records]
n_ok, n_skip, n_miss, n_err = 0, 0, 0, 0
with Pool(args.workers) as p:
for i, (vid, status) in enumerate(p.imap_unordered(_process_clip, tasks), 1):
if status.startswith("ok"):
n_ok += 1
elif status == "skip":
n_skip += 1
elif status == "missing":
n_miss += 1
else:
n_err += 1
if i % 10 == 0 or i == len(tasks):
print(f" [{i}/{len(tasks)}] {vid}: {status}", flush=True)
print(f"\nDone: ok={n_ok} skipped={n_skip} missing={n_miss} errors={n_err}")
if __name__ == "__main__":
main()
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